Evaluation of fisheries management and sustainability indicators can be supported by a reliable index of harvest rate. However, the most appropriate model that accounts for recreational fisheries is largely unknown. In order to adjust for these factors, generalized linear models were applied to data from shore-based recreational fishing surveys conducted in Western Australia between 2010 and 2016. Five candidate error distributions (lognormal, Gamma, Zero-Altered Gamma, Tweedie, and delta-lognormal) and seven independent variables (year, month, target species, fishing platform, fishers’ avidity, time of day, and day type) were examined for commonly caught nearshore teleost species. Zero-Altered Gamma and Tweedie models performed best overall, although model performance and explanatory variables varied between species. Standardized harvest rates for Australian herring (Arripis georgianus) declined from 1.88 ± 0.17 (mean ± s.e.) fish per fishing party per day) in 2010 to 0.86 ± 0.07 in 2016, while harvest rates for School whiting (Sillago spp.) increased from 0.44 ± 0.21 in 2010 to 0.94 ± 0.34 in 2016. The standardized harvest rates for both species generally showed less fluctuation among years and consistently had smaller errors than the raw harvest rates. Overall, the results suggest that the choice of error distribution, as well as explanatory variables, is species dependent when assessing shore-based fisheries. The approach used could easily be adapted to other recreational fisheries to establish reliable species-specific harvest rates that can detect variability against thresholds set in harvest strategies.
Alissa Tate, Johnny Lo, Ute Mueller
Ices Journal of Marine Science